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AI: The Final Frontier in Customer Experience or the Portal to a Brave New World?

Over the years,
the one constant in the customer care arena has been technological change. It
has evolved from the dawn of rudimentary call centers in the ‘60s and ‘70s, and
grown via advances in touch-tone dialing and toll-free service that led to IVR
technology.It has ridden the wave of
domestic and later international outsourcing that began in the ‘90s, taken new
paths with the broadened spectrum of service channels made possible by the
internet, and reached higher levels with the introduction and universal
adaptation of customer support software and sophisticated routing solutions
over the past two decades, the increasing need for mobile customer service and
more.

Throughout all
this progress, the common thread has been the need for human agents to manage
the interactions, deliver the answers, and be the front-line link between
companies and their customers. Now the landscape is shifting rapidly and the
relentless stream of advanced algorithms is changing the equation. Is this the
time that the long-predicted mass migration to AI-based entities – referred to
as intelligent virtual assistants (IVAs), chatbots,
virtual agents, and a variety of other terminologies – will begin in earnest?

It’s
no secret that AI entities can enable organizations to increase efficiencies
and reduce costs, making it possible to deliver quality service without having to
maintain 24/7 contact centers staffed by employees and contractors all over the
world. Gartner has made the bold prognostication that by 2020, 85% of all
customer interactions will no longer be managed by humans. In an often-cited2016 worldwide
survey by Xerox(now
Conduent), 42% of respondents predicted that with the move toward wider use of
AI, the contact center as we know it now will cease to exist by 2025. “AI is the next wave of the industrial
revolution,” said Yi Zhang, CEO of Rulai and an early proponent of the
technology.

Billions of dollars
are being poured into the development of AI-based customer service solutions and
next-gen IVAs with more self-learning capabilities are already making their way
to the market. These emerging entities rely on natural language processing
(NLP) and machine learning technologies that enable them to grow smarter over
time and adapt to customers’ individual preferences as they “learn” from past
interactions to improve their understanding of customer expectations and needs.

According to
Dan Mitus, Technical Solutions Manager, Luminoso, AI and natural language understanding
specialist, the most effective solutions use AI and NLP to understand language
the way a human does. “The goal is having people be able to converse with a
machine in a way that makes them feel comfortable … offering a solution that
can interpret historical experiences and maintain context,” he said. “The
products that helped introduce AI to the public such as Siri and Alexa allow
people to do things that are very transactional in nature. Ultimately, the goal
is to introduce more conversational solutions that use natural language more
effectively. That’s a big challenge and much of the NLP research is going to
provide deeper understanding that will enable machines to conduct more robust
conversations.”

Understanding
customer intent is also an important attribute, with Mitus believing that the
ability to do so helps to encapsulate the human experience. “Contact centers
offer great insights about a product or service. You’re not going to get any
better input than the honest feedback that people provide when getting in touch
to talk about their problems,” he said. “A
solution should be able to understand not just what people want but the reasons
they want it…the how’s and the why’s.”

At
this juncture, the most prevalent use of AI solutions in the contact center is
to automate routine tasks and enhance service delivery. “I think back to the
days of phone trees, when customers were screaming into the phone for a live
agent because the machine didn’t understand them,” said Mitus. “Being able to
automate the process has a significant business value, but the solution must
meet the challenge of always understanding the customer. In most cases, one
single negative self-service experience means that the caller will go back to
asking for a live agent every time and the opportunity for them to self-serve on
an ongoing basis will have been missed.”

Successful
implementation of AI in contact center environments produces the desired
savings by reducing the need for live agents to handle calls, as well as
freeing them for more meaningful work. In financial institutions, for example,
agents have been spared such mundane responsibilities as providing account
balances or helping people make transfers or payments.

Despite the
current and future benefits of AI, many companies remain on the sidelines.
While a recent ICMI/Oracle study revealed 85% of respondents said they would
like to see their organizations adopt or expand the use of AI. Accenture research
showed that 56% of companies are in what they call the “observer”
stage. These organizations do not fully see the transformational or incremental
value of AI and are undertaking relatively small initiatives with a “wait and
see” approach.

“A lot of
businesses have existing infrastructure, processes and people based around
systems that they’ve already built,” said Mitus. “It’s difficult for them to
make the necessary changes to incorporate AI, but it’s the legacy systems that
are inflexible, not the AI.” He believes that organizations need to create a
clean path that enables them to move forward from treating contact centers as
cost centers to supporting them with AI solutions that can reduce costs and
create new opportunities. “Designing the
right process to set up a transitional phase where businesses can seamlessly
integrate AI and machine learning applications enables them to leverage all of
the capabilities that are now available,” he said.

In addition to
call deflection and low-level self-service, Mitus believes that companies
should explore AI to make human agents more efficient, put better resources at
their fingertips, help them better understand the customer, and automatically
detect when a customer needs to be escalated to a supervisor. “These are
intermediary steps toward more automated self-service but can add a lot of
value toward changing the organizational perception of contact centers as
strictly cost centers.”

FabioCardenas, CEO of Sundown AI, whose self-learning Chloe automation layer is
designed to help companies answer consumer questions and send notifications via
voice, chat and SMS, believes that companies need to start by determining what
operations they have that are purely transactional. “At the lowest level these are
questions that could be answered by FAQs; at the next level are questions that
need to be customized and require database lookup to be answered in real time.
The next layer would be taking action on behalf of the user, such as closing a
case or sending out notifications. These are the base use cases for incorporating
AI.”

To
provide service for more complex products or higher-level information requests,
Cardenas believes that companies need to get more advanced systems that can employ
machine learning and discern customer intent, analyze patterns and customize information
from specific customer profiles. “In higher level systems with machine
learning, you are able to train the system to identify intent. The beauty of
that is that once it is trained, it is able to deliver intent on any future
data that it receives without anything done manually; the system can then
provide suggestions on how to improve customer satisfaction.” He cites QA as an
example of one area where this is particularly valuable, with machine learning enabling
companies to more quickly examine and share scorecards of conversations, rate
them all (as opposed to the small percentage reviewed by manual procedures) and
compare agent-to-agent performance. This can produce exceptional value in
preventing negative outcomes.

Perhaps
the most pressing question about implementing AI solutions is how quickly they
can produce ROI. While acknowledging that results depend on the size of the training
set, on the complexity of the applications and the use cases, Cardenas said
that the costs of the Sundown.ai system… which is mainly used by SMBs as
opposed to enterprise clients… starts at about the salary of an employee for
two months and claimed it could begin producing returns over a 3- to 6-month
period. Both ROI and efficiency are improved by an AI solution’s ability to
provide 24/7 service over a variety of channels and clear a backlog of cases
and service tickets that have been received. For example, Sundown cited improving
resolution rates from the mid-40% range to 85-90% over a two-month period for
two international clients.

To Rulai, which takes a different approach by providing companies with an
Interaction Design Console that enables non-technical users to design chatbots
without the need for coding, the decision to implement AI should be based on a
cost-benefit analysis. This should be used to determine which use cases are
more frequent and which ones would ultimately be the most successful ones for deploying
bots. They offer a build-a-task application to help businesses test the use
case and offer an adaptive learning mode to help companies improve bot
effectiveness.

Rulai
believes in empowering the business user to develop bots without having to rely
on internal IT. “The idea is to put the domain expert—the CX leader-- in
control of AI,” said Rulai CEO Yi Zhang. “Most mid-market companies do not
have much in the way of IT resources nor the budget to implement solutions
without knowing whether they are going to produce results. CX leaders want to manage
the strategic imperative of integrating AI over the long haul; they want to set
the pace for rolling out virtual assistants in conjunction with live agents and
how to augment the capabilities of human agents. The idea is to remove the
friction in the process.”

“Deep
learning originates from a company’s networks and the amount of data available
to pre-train the model,” said Zhang. “It starts with general understanding of
syntax, semantics and dialogue flows to help in determining customer intent.
The additional computing power now at our command makes it more effective. In
some areas, deep learning is achieving better performance than human beings in
such tasks as sentiment analysis based on text and speech. It’s better at
predicting survival rates in medicine, in gambling and computer games. Natural
language processing is the holy grail in AI,” she said. “It takes static
customer information and integrates it with dynamic changing information and
combines it with ongoing customer behavior to help predict intent and maintain
context.

Many
of the real-world implementations of the Rulai build-your-own bot solution
begin at the top of the business chain with a VP of customer experience or
contact center operations. Once on board, the executive then appoints a
“service design manager,” creating a new opportunity for someone who is viewed as
the one of the best and brightest in the contact center, according to Rulai
customer evangelist Jim Diaz, “This person is charged with keeping tabs on the
best practices of implementing and managing bots. He or she is also responsible
for knowing where the data is and how to use it.” Rulai helps to further
empower these service design managers with training that helps them effectively
become what Diaz called an “AI project manager.” They are taught how to manage
the console, building out tasks using simple drag-and-drop techniques and
pulling in data. According to Diaz, their main responsibility will be approving
recommendations into the console that affect the training and deployment of the
bot. Essentially, service design managers monitor the performance of the bot
and are aware of situations where the bot needs tweaking to facilitate
necessary adjustments.

Zhang
cited a recent McKinsey report called “Artificial
Intelligence: The Next Digital Frontier?” which estimated the annual
investment in developing AI technology last year at $26 to $39 billion (which has tripled over the past three years). She noted that a significant portion of
this spending often goes to bringing in AI specialists to make it work. “The
average cost of hiring an AI expert for a company was between $5 and $10
million,” she said. Zhang believes that not only can this expense be dramatically
reduced by putting AI in the hands of a domain expert who is focused on the
customer experience, but the results will ultimately be superior. “The domain
expert needs to be the leader, not the engineer.”

Of
course, even if alternative opportunities are created for some workers and
there is a greater need for higher level agents to address more complex
inquiries, implementing AI technology will have a serious negative impact on
contact center employment. “Even when a self-learning system needs to escalate
for a question that is not currently in the database, the resulting answer is
then added to the system’s body of knowledge, which lessens the need for humans
to handle these tasks.” said Sundown’s Cardenas. “This will ultimately affect overall
headcount, which will also have an effect on rates charged by SaaS providers.
Companies don’t want to manage acres of cubicles when there are cost-effective
alternatives.” Over the next 3 to 5 years, Cardenas sees a reduction in live
agents of 30% to 45%, particularly in areas like India and the Philippines
where contact centers handle more repetitive transactions. “Jobs that require a
greater degree of expertise should survive in the short run,” he noted. “But
over 10 years, who knows?”

There
are numerous business cases that can be made on how AI can deliver significant value
to companies of all sizes willing to use it in customer-facing applications. In
addition, by providing live agents the resources they need to answer more
complex inquiries more quickly and accurately, it can contribute to greatly
improved service on all levels. For now, most industry observers believe that
while the technology has already disrupted the way contact centers are operated
and staffed, AI is not yet positioned to displace the need for human
agents.